Blockchain Technology Adoption in Canadian Pharmaceutical Sectors: An empirical analysis for a future outlook
Bibliographic record
Abstract
There are many calls in the literature to investigate the Blockchain technology adoption (BCT) in Canadian Organizations and its impact on boosting enterprises' competitive advantages.Although the literature requires more research cases, it is more timely and relevant that the analysis be done as early as today.Various empirical supports for Technology Acceptance Model (TAM) are available depending on situation specifics.TAM remains a widespread and convenient theoretical framework for examination of aspects contributing to technology acceptance.This study aims to find the driving forces that effectively illustrate the blockchain technology adoption in Canadian Pharmaceutical Organizations and to be able to face the challenges associated with the process of adoption.This study examined BCT application using contacts from Canadian Companies Capabilities directory (CCC) and applied SEM regression using AMOS software with 750 respondents from pharmaceutical businesses using TAM framework.Path analysis results were good: chi2 (4918.592),chi2 / DF (5.513), RMSEA (0.049), CFI (0.753), and TLI (0.804).Perceived ease of use, Perceived Usefulness, attitude towards use, and intention to use predicted BCT utilization, yet two relationships (i.e., PEOU->PU and PU->IU) were rejected in the tested model as they show negative conformity results.All components explain more than 50% of variation, hence presenting a reasonable fit between the data examined and the research model.These findings will help in understanding of pharmaceutical organizations' adoption of BCT for researchers, regulators and developers and providing supported evidence on factors contributing to the adoption of BCT in Canadian Organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".